Background
Experience across risk and delivery.
Roles across financial risk analytics and earlier online trading firms show how I move from
technical detail to reliable execution, commercial awareness, and clear stakeholder communication.
2020 — Present
Financial risk analytics
Hedge-Tech | Fin-Tech & Risk Management
Analyze large financial datasets; review and improve automated risk-analysis scripts; validate
inputs, outputs, assumptions, and reporting logic; support financial-instrument modelling,
ALM/liquidity and uncertainty workflows; investigate data and process issues; and deliver
stable, documented outputs for major financial institutions.
The role also requires coordination with client-side technology teams, regulation-related
reporting support, root-cause analysis, issue escalation, and clear recommendations for both
technical and business stakeholders.
2017 — 2019
Project & stakeholder management
GTP Solutions | Online trading firm
Coordinated projects in an online trading environment across sales and marketing activity,
team workflows, stakeholder communication, and client and employee relationships. Followed
delivery across multiple priorities and kept commercial and operational work moving clearly
between stakeholders.
2015
Client-facing sales
DMD Online | Online trading firm
Handled client-facing sales and service in an online trading environment, explaining offerings,
identifying customer needs, and maintaining clear follow-up throughout the sales process.
MA
Statistics and Data Science · In progress
Statistical modelling, machine learning, optimization, stochastic processes, simulation,
predictive analytics, deep-learning methods (MLPs, CNNs, and RNNs) with PyTorch and TensorFlow,
and model evaluation.
BA
Economics and Statistics · Completed
Economics, probability, inference, quantitative reasoning, financial markets, and
data-informed decision-making.
Practice
Risk, research and data science.
Risk management defines exposures, controls, limits, and accountability. Statistics measures
evidence, uncertainty, and predictive performance. Research connects both to stronger questions,
transparent methods, and practical decisions.
R
Risk practice
Risk management
Financial-risk analysis, controls, modelling, validation, and decision-ready reporting.
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Liquidity & ALM: ALM and maturity ladders, LCR, NSFR, ASF/RSF, cash-flow
gaps, funding stability, limits, and liquidity scenarios.
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Credit risk: PD/LGD/EAD, expected loss, scorecards, limits, vintage and cohort
monitoring, and early-warning signals.
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Market risk: Instruments, sensitivities, VaR and Expected Shortfall
foundations, limits, stress scenarios, and portfolio interpretation.
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Enterprise & operational: Risk appetite, KRIs, controls, incidents,
IT/data risk, root-cause analysis, and remediation.
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Model & reporting risk: Assumptions, benchmarks, backtesting, monitoring,
data lineage, reproducibility, and escalation.
S
Research & modelling
Statistics & data science
Applied statistics, research, predictive modelling, validation, and reproducible analysis.
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Statistical methods: Probability, inference, hypothesis testing, regression,
stochastic processes, simulation, and uncertainty.
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Predictive modelling: Trees, Random Forest, XGBoost, multilayer perceptrons
(MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), segmentation,
and calibrated probabilities.
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Validation: Leakage-safe cross-validation, benchmarks, metrics, calibration,
diagnostics, feature importance, and stability.
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Research & experimentation: A/B testing, sample-size reasoning, causal
foundations, cohorts, funnels, and root-cause analysis.
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Data workflows: EDA, cleaning, validation, automated checks, reproducible
code, dashboards, and technical reporting.
The shared ground
Where risk and statistics become one practice
Uncertainty
Scenarios
Sensitivity, Monte Carlo, baseline/adverse/severe scenarios, reverse stress, tail-risk
reasoning, and explicit assumptions.
Models
Evidence with controls
Fit and predictive quality are paired with validation, monitoring, limitations, governance,
human checkpoints, and defensible use.
Decisions
Signals people can act on
Complex results become limits, alerts, risk queues, operating thresholds, clear trade-offs,
and concise stakeholder recommendations.
Risk method set
Controls around the calculation.
Core methods for testing models, understanding exposures, monitoring limitations, and producing
reliable risk decisions.
Core practice
Model validation, backtesting & monitoring
Test conceptual soundness, implementation, outcomes, stability, and limitations; compare
observed performance with expectations and benchmarks; and document required action.
- Backtesting
- Benchmarking
- Outcome analysis
- Calibration
- Drift & stability
- Sensitivity analysis
- Stress testing
- Limitations
- Issue tracking
Risk modelling
Financial exposures & scenarios
Model cash flows, funding, credit and market exposures across ordinary, adverse, and reverse
stress conditions.
- ALM / maturity ladder
- LCR
- NSFR
- ASF / RSF
- PD · LGD · EAD
- Expected loss
- VaR / Expected Shortfall
- Scenario analysis
- Monte Carlo
- Uncertainty modelling
Controls & reporting
Governance around the output
Keep risk work accountable through limits, controls, data quality, reproducibility,
documentation, escalation, and tracked remediation.
- KRIs
- Limits & thresholds
- Data lineage
- Reproducibility
- Reconciliations
- Automated reporting
- Root cause & remediation
- Regulatory documentation